@article {ElMahdi2020, title = {Optimized Scheme to Secure IoT Systems Based on Sharing Secret in Multipath Protocol}, journal = {Wireless Communications and Mobile Computing}, volume = {2020}, year = {2020}, note = {cited By 1}, abstract = {Internet of Things (IoT) is a hot and emerging topic nowadays. In the world of today, all kinds of devices are supposed to be connected and all types of information are exchanged. This makes human daily life easier and much more intelligent than before. However, this life mode is vulnerable to several security threats. In fact, the mobile networks, by nature, are more exposed to malicious attacks that may read confidential information and modify or even drop important data. This risk should be taken in consideration prior to any construction of mobile networks especially in the coming 5G technology. The present paper aims to provide a contribution in securing such kinds of environment by proposing a new protocol that can be implemented in ad hoc networks. {\textcopyright} 2020 Fatna El Mahdi et al.}, keywords = {5G mobile communication systems, ad hoc networks, Confidential information, Daily lives, Emerging topics, Internet of things, Internet of Things (IOT), Malicious attack, Mobile security, Mobile telecommunication systems, Multipath protocols, Network security, New protocol, Security threats}, doi = {10.1155/2020/1468976}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083513010\&doi=10.1155\%2f2020\%2f1468976\&partnerID=40\&md5=a7326029a67a6d4fc68c90a958148708}, author = {El Mahdi, F. and Habbani, A. and Kartit, Z. and Bouamoud, B.} } @article {KamalIdrissi201642, title = {CKMSA: An anomaly detection process based on K-means and simulated annealing algorithms}, journal = {International Review on Computers and Software}, volume = {11}, number = {1}, year = {2016}, note = {cited By 0}, pages = {42-48}, abstract = {In modern years, countless researchers are interested in anomaly detection techniques for building intrusion detection systems (IDS). Intrusion detection is a process of recognizing attacks and intrusions. The IDS key purpose is to classify the Regular and Intrusive activities. Anomaly based IDS are built on an approach including first training a system with data in order to establish a certain view of normality and then use the determined profile on actual data to flag non-conformities. However, those kinds of IDS are highly vulnerable to mistaken alerts and present at the same time a very low detection rate when the learning is performed on misclassified data. Therefore, the need for an underlying clustering algorithm, which can process optimally the data grouping, is on agenda. In our paper, we combined two methods of clustering and optimization, namely K-means and Simulated Annealing, in order to achieve a global optimum classification for the data subject to learning and consequently avoid being limited to local optimum solutions. The K-Means in this work is used in its semi-supervised variant in order to lessen the number of times that the algorithm is applied and thus keep our work likely to be used in real time context. The developed algorithm has produced satisfactory results when applied on NSL-KDD data set, the tests reveal this method can enhance the detection and misdetection rates of intrusion detection systems. {\textcopyright} 2016 Praise Worthy Prize S.r.l. - All rights reserved.}, doi = {10.15866/irecos.v11i1.8272}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964303661\&doi=10.15866\%2firecos.v11i1.8272\&partnerID=40\&md5=513c0be5ae8455ca16c42fff3214e690}, author = {Kamal Idrissi, H. and Kartit, Z. and Kartit, A. and El Marraki, M.} }